Thank you for the very nice write-up of challenges that car manufacturers face with additive manufacturing adoption – I enjoyed reading it. While there’s been a lot of buzz in the recent years about the potential of additive manufacturing to disrupt current status quo, I actually have seen very few (if any) examples of successful commercialisation of additive manufacturing at scale. For the automotive industry, as you mentioned, the main competitive advantage that additive manufacturing provides might be with product design rather than shortened product development life-cycle / cost optimization. With that said, the ultimate question I have is how much demand is it out there for fully customized cars that come at substantially higher cost (vs. traditional vehicles, at least in the beginning)? While I can see how high-end car manufacturers with established premium brand names might be able to benefit from this overall trend of personalization, the value proposition doesn’t seem to hold for the vast majority of lower- and medium-end manufacturers, and thus putting a limit on the total market opportunity.
Thank you – I thought the essay provided a good overview of potential areas of machine learning disruption within the context of investment banking. There are many sub-groups and divisions within an investment bank, each specializing in different activities and thus exposed to AI disruption at varying degree. While I agree that certain functions will inevitably be automated in the future (disproportionately back and middle-office functions), the role of an investment bank is unlikely to go away. Let’s take sell-side M&A investment banking for example. At the very core, the services that an investment bank provide in that case are i) executive relationships to top buyers in the relevant industries, and ii) sell-side advisory and negotiation that guarantees the seller the best deal. Both are very human-centered activities and cannot be easily replaced by machine algorithms.
Thank you for bringing to our attention a very interesting (and arguably commercially successful) use case of Open innovation. Amazon has in the process turned Alexa into a platform, the same way Apple and Google have turned their Apple store and Google Play into sticky platforms for development of 3rd party apps. Unlike some others, I’m not too concerned that Alexa voice assistant usage lags that of Apple and Google. I believe a more accurate metric would be monetization ability, which of course is harder to track and not as openly publicized. In terms of monetization, Alexa has a leg up over Apple Siri and Google assistant because it’s directly plugged into the massive ecosystem of Amazon commerce, while the same cannot be said about Apple or Google. You already bought a massively overpriced Apple iPhone, what else can you buy from Apple on a day-to-day basis?
However, at some point, Alexa will have to move beyond its market in the home. The real challenge then is how Alexa can secure its place as a gateway to Amazon services on the smartphones, while those platforms are controlled by Apple and Google. We can already see Facebook struggling with the same issue, as evidenced by their latest product release of the Portal, e.g., their attempt to more directly control the interface with customers.
Very well-researched piece. I enjoyed reading it – thank you! Some thoughts:
1) I wonder the degree to which open innovations are integrated into the Grupo Aval strategy / strategic initiatives. The one issue I’ve seen with big companies and open innovations is that bootcamp-types of events are often hosted one-off, with great fanfare, to drive up internal interest in innovation and also generate positive PR, but not fully formalized and integrated into the annual executive agendas. You mentioned Grupo Aval hosted their first bootcamps in 2014 – I’m curious to see 4 years after what the outcomes of the winning projects are, and whether they’ve consistently hosted other bootcamps the year after.
2) Startups really have a hard time retaining their competitive edge once acquired by a big company. I can recall examples of startups who have been able to scale much more quickly once acquired, but the innovation spirit essentially is killed as it becomes part of a big corporation. Ideas are not hard to come by these days – it’s the execution that counts. I wonder what can be done to ensure high-potential ideas are successfully executed, at speed, inside a big corporate post open innovation.
Thank you for the write-up! This is a new application of 3D printing at a commercial scale that I did not know about – always good to learn. 2 thoughts that I have
1) As mentioned by some of the comments above, the rate of adoption of additive manufacturing has been slow. I wonder what the core drivers of this are: a) is it that the current technology not being sophisticated enough for commercial manufacturing? or b) is the current cost associated with 3D printing is still uncompetitive vs. traditional manufacturing? But it is not just commercial 3D printing that hasn’t gained much traction. Glowforge, a 3D printing company based out of Seattle, makes a different bet: they believe in the future of personalization happening at the home and go after a B2C model. They have had multiple product shipment delays in the past, and as far as I’m aware, haven’t seen meaningful customer uptake. But maybe we just need to wait.
2) You mentioned additive manufacturing allows Chanel to respond to important trends in beauty, namely indie brands launched by influencers with minimal marketing budget. I’m curious if scale adoption of 3D printing will even further feed the growth of this trend and drive down the overall costs involved in product development and manufacturing of the entire industry. As barriers to entry go down with new manufacturing technologies, additive manufacturing being one of them, I see smaller indie brands benefiting more from this, vs. the corporate behemoths.
Very interesting use case of machine learning that you’ve brought up. I have 2 questions / thoughts that I’d like to share:
1) Social Capital is one of the first larger VC funds that experimented with using machine learning to do deal screening and investments. It was co-founded by Chamath Palihapitiya, former Facebook Executive, and he likes to embody the contrarian voice in the VC world. Social Capital went through a major shake-up recently with the vast majority of investing partners leaving the firm (except for Chamath). Social Capital has stopped raising funds and announces that they now will only invest using their own internal money. As outsiders, we don’t have enough information to know what the root causes were, but there was speculation around internal disagreements regarding investment approach. VC appetite for adoption of machine learning to inform their own investment looks to be varied, at this stage at least.
2) Machine learning, and especially with the recent progress in deep learning, have proven to be good at predicting trends, based on past inputs. The big assumption as discussed in our TOM class is that the relationships in the training dataset hold with the new data that we’re hoping to predict. VC is an industry whose returns is driven by its ability to look 10, 20 years into the future and predict disruption. I do believe we can all benefit from bringing more data and rigor to the investment process and thus mitigating VCs’ biases (both conscious and unconscious). However, I don’t believe the final investment decisions are something that can be entirely driven by machine learning, without human expertise.